Mine goaf gas monitoring method and device, electronic equipment and storage medium

Abstract

The invention provides a mine goaf gas monitoring method and device, electronic equipment and a storage medium, and belongs to the technical field of mine monitoring. The method comprises the steps that gas data collected by sensors arranged in different areas of a goaf are acquired, and a gas concentration matrix with time and space correlation is generated based on the gas data; establishing a gas diffusion model based on geologic structure data and ventilation parameters of the goaf, and inputting gas data into the gas diffusion model to calculate a gas concentration prediction matrix of the goaf; determining a target monitoring strategy based on a deviation coefficient matrix of the gas concentration matrix and the gas concentration prediction matrix; and monitoring the goaf based on the target monitoring strategy. According to the mine goaf gas monitoring method and device, the electronic equipment and the storage medium, the accuracy of mine goaf gas monitoring is improved.

Description

Technical Field

[0001] This application belongs to the technical field of mine monitoring. More specifically, it relates to a method and device for monitoring gas in a mined - out area of a mine, an electronic device, and a storage medium. Background Art

[0002] During the process of mine exploitation, the gas condition in the mined - out area is directly related to the safe production of the mine. As the mining activities progress, various harmful gases such as gas and carbon monoxide will accumulate in the mined - out area. If they cannot be effectively monitored and controlled, it is extremely easy to cause serious safety accidents such as explosions, poisoning asphyxiation, etc.

[0003] At present, most of the gas monitoring methods in the mined - out area of mines use single - point sensors for decentralized monitoring, which is difficult to comprehensively reflect the overall gas concentration distribution and dynamic change law in the mined - out area, and there are problems of monitoring blind spots and lag. In addition, the existing monitoring means cannot adjust the monitoring focus according to actual needs, making it impossible to rationally allocate monitoring resources and difficult to meet the increasingly complex requirements of mine safety production.

[0004] Therefore, there is an urgent need for an accurate method for monitoring gas in the mined - out area of mines. Summary of the Invention

[0005] The purpose of this application is to provide a method and device for monitoring gas in a mined - out area of a mine, an electronic device, and a storage medium to improve the accuracy of gas monitoring in the mined - out area of a mine.

[0006] In the first aspect of the embodiments of this application, a method for monitoring gas in a mined - out area of a mine is provided, including: Obtain gas data collected by sensors set in different areas of the mined - out area, and generate a gas concentration matrix with time and space correlations based on the gas data; Establish a gas diffusion model based on the geological structure data and ventilation parameters of the mined - out area, and input the gas data into the gas diffusion model to calculate the gas concentration prediction matrix of the mined - out area; Determine the target monitoring strategy based on the deviation coefficient matrix of the gas concentration matrix and the gas concentration prediction matrix; Monitor the mined - out area based on the target monitoring strategy.

[0007] In the second aspect of the embodiments of this application, a device for monitoring gas in a mined - out area of a mine is provided, including: A matrix generation module, configured to obtain gas data collected by sensors set in different areas of the mined - out area, and generate a gas concentration matrix with time and space correlations based on the gas data; A prediction matrix module, configured to establish a gas diffusion model based on the geological structure data and ventilation parameters of the mined - out area, and input the gas data into the gas diffusion model to calculate the gas concentration prediction matrix of the mined - out area; A strategy determination module, configured to determine a target monitoring strategy based on a deviation coefficient matrix of a gas concentration matrix and a gas concentration prediction matrix; A gas monitoring module, configured to monitor a gob area based on the target monitoring strategy.

[0008] In a third aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned gas monitoring method for a mine gob area are implemented.

[0009] In a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned gas monitoring method for a mine gob area are implemented.

[0010] The beneficial effects of the gas monitoring method, device, electronic device, and storage medium for a mine gob area provided by the embodiments of the present application are as follows: By collecting gas data through sensors arranged in different areas of the gob area and generating a gas concentration matrix with time and space correlation, the present application can accurately capture the dynamic changes of gas concentrations at different time points in each area and avoid monitoring blind spots. The present application establishes a gas diffusion model based on geological structure data and ventilation parameters, uses the model to simulate the gas diffusion law, and combines the actual collected data to predict the gas concentration, making the prediction results more in line with the real situation. The present application also determines the monitoring strategy according to the deviation coefficient matrix of the gas concentration matrix and the prediction matrix, can timely adjust the monitoring focus for deviations, and make up for prediction errors. At the same time, the target monitoring indicators are determined based on the mining information and gas hazard levels, focusing on key gas parameters and reducing unnecessary interference factors. By combining the target monitoring strategy with the indicators, the accuracy of monitoring is improved, providing accurate data support for mine safety management and effectively reducing the risk of safety accidents caused by gas problems. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 It is a schematic flowchart of a gas monitoring method for a mine gob area provided by an embodiment of the present application; Figure 2 It is a structural block diagram of a gas monitoring device for a mine gob area provided by an embodiment of the present application; Figure 3Schematic block diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0013] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0014] To make the objectives, technical solutions, and advantages of the present application clearer, the following will be described through specific embodiments with reference to the accompanying drawings.

[0015] Please refer to Figure 1 , Figure 1 Flow schematic diagram of a gas monitoring method for a mined - out area of a mine provided by an embodiment of the present application. The method includes: S101: Obtain gas data collected by sensors set in different areas of the mined - out area, and generate a gas concentration matrix with time and space correlations based on the gas data.

[0016] In this embodiment, a variety of types of sensors are used, which are arranged in different areas of the mined - out area (such as positions near the air inlet, air outlet, roof, near the roadway wall, and deep in the mined - out area), including but not limited to carbon monoxide sensors, gas sensors, oxygen sensors, hydrogen sulfide sensors, etc. The gas data is collected at a set sampling frequency (for example, once per minute). In this embodiment, according to the collected gas data, as well as the geographical location information and collection time information of the sensors, a gas concentration matrix with time and space correlations is constructed; where the gas concentration matrix has time series as rows and the spatial positions of different sensors as columns, and the data shown is the gas concentration data of each area in the mined - out area at different times.

[0017] In this embodiment, before deploying the sensors, a three - dimensional model of the mined - out area is built using a Geographic Information System (GIS). Combining with the historical mining data of the mined - out area, the distribution law of past gas anomaly areas is analyzed. At the same time, special geological structure areas such as the roof caving zone and fracture development zone in the mined - out area are considered to obtain the deployment positions of the sensors. In addition, for areas in the mined - out area with potential hidden dangers of high - temperature fire sources, temperature sensors are added to avoid the occurrence of fires; at the connection points between the mined - out area and adjacent mined areas and roadways, the density of sensors is increased to prevent gas from flowing to other areas.

[0018] In this embodiment, for the original gas data collected by the sensor, validity verification is first performed to eliminate invalid data caused by sensor failures, communication anomalies, etc. Secondly, the sliding average filtering algorithm is used to smooth the data and remove random noise in the data. Finally, for missing data, linear interpolation or a prediction model based on machine learning is used to complete the data according to the data at the previous and subsequent time points and the data of sensors at adjacent positions, ensuring the integrity and continuity of the data.

[0019] When constructing the gas concentration matrix in this embodiment, the gas data can also be associated with the production activity data of the mine (such as coal mining operation time periods, blasting times, etc.) and meteorological condition data (such as atmospheric pressure, ambient temperature, etc.). For example, during coal mining operations, record the gas concentration changes in each area during this period and analyze the impact of production activities on gas concentration. At the same time, consider the impact of changes in atmospheric pressure and ambient temperature on gas diffusion, and incorporate these factors as additional dimensions into the gas concentration matrix to form a multi-dimensional data matrix that more comprehensively and accurately reflects the gas change law in the goaf.

[0020] S102: Establish a gas diffusion model based on the geological structure data and ventilation parameters of the goaf, and input the gas data into the gas diffusion model to calculate the gas concentration prediction matrix of the goaf.

[0021] In this embodiment, the collected geological structure data of the goaf includes: the shape, size, rock layer distribution, fracture development conditions, etc. of the goaf. At the same time, obtain the ventilation parameters of the goaf, such as ventilation air volume, ventilation method (exhaust type, forced type or hybrid type), air flow direction, etc. Based on the above geological structure data and ventilation parameters, a gas diffusion model is established using the principles of computational fluid dynamics or other suitable mathematical model methods. The gas diffusion model constructed in this embodiment can simulate the diffusion, migration and mixing processes of gas in the complex environment of the goaf. In this embodiment, the collected gas data is used as the input of the model, and the gas diffusion model is solved by numerical calculation and other methods, so as to calculate the gas concentration prediction matrix of the goaf in the future for a period of time (such as the next 1 hour, 2 hours, etc.). This matrix also contains time and space dimension information, and predicts the change trend of gas concentration in each area.

[0022] S103: Determine the target monitoring strategy based on the deviation coefficient matrix of the gas concentration matrix and the gas concentration prediction matrix.

[0023] In this embodiment, calculate the deviation coefficient matrix between the gas concentration matrix and the gas concentration prediction matrix. By comparing the elements at the corresponding positions of the two matrices, appropriate error calculation methods such as mean square error and absolute error are used to quantify the difference between the actual gas concentration and the predicted gas concentration.

[0024] In this embodiment, the change characteristics and abnormal conditions of gas concentration are analyzed based on the deviation coefficient matrix; if the deviation coefficient of a certain area is large, it indicates that the actual value of the gas concentration in this area is significantly different from the predicted value, and there may be potential hazards such as abnormal ventilation and gas leakage.

[0025] In this embodiment, the target monitoring strategy is determined according to the analysis results, specifically including: for areas with a large deviation coefficient, increasing the layout density of sensors or raising the sampling frequency of sensors to obtain gas data in this area more accurately and timely; for areas where the change trend of gas concentration is unstable or there are potential hazards, arranging manual inspections or using equipment such as drones for key inspections; adjusting the parameters of the ventilation system to optimize the ventilation effect and reduce the risk of accumulation of dangerous gases.

[0026] S104: Monitor the goaf based on the target monitoring strategy.

[0027] In this embodiment, the goaf is monitored according to the determined target monitoring strategy, gas data is obtained and updated in real time, the gas concentration matrix and the gas concentration prediction matrix are continuously analyzed and compared, and the monitoring strategy is dynamically adjusted to ensure that gas safety hazards in the goaf can be detected and handled in a timely manner, and the safe production of the mine is guaranteed.

[0028] Exemplarily, it is assumed that 3 sensors at different positions are set in the goaf (denoted as sensor A, sensor B, and sensor C respectively), and carbon monoxide (CO) gas concentration data (unit: ppm) is collected at 4 different times (denoted as time 1, time 2, time 3, and time 4).

[0029] According to the actually collected data, the constructed gas concentration matrix is as follows:

[0030] Among them, the rows of the matrix represent different times (from top to bottom are time 1, time 2, time 3, and time 4 in sequence), and the columns of the matrix represent different sensors (from left to right are sensor A, sensor B, and sensor C in sequence).

[0031] Based on the gas diffusion model, the carbon monoxide gas concentration at the same 4 future times and 3 positions is predicted, and the obtained gas concentration prediction matrix is as follows:

[0032] The meanings of the rows and columns of the gas concentration prediction matrix are the same as those of the gas concentration matrix; by comparing these two matrices, the deviation coefficient matrix can be further calculated to analyze the difference between the actual situation and the predicted situation, so as to determine the monitoring strategy.

[0033] It can be concluded from the above that in this application, gas data is collected by sensors set in different areas of the goaf, and a gas concentration matrix with time and space correlation is generated, which can accurately capture the dynamic changes of gas concentration at different time points in each area and avoid monitoring blind spots. A gas diffusion model is established based on geological structure data and ventilation parameters, and the gas diffusion law is simulated using this model. Combining the actual collected data to predict the gas concentration, the prediction result is more in line with the real situation. The monitoring strategy is determined according to the deviation coefficient matrix of the gas concentration matrix and the prediction matrix, and the monitoring focus can be adjusted in time for the deviation to make up for the prediction error. At the same time, the target monitoring indicators are determined based on the mining information and the gas danger level, focusing on key gas parameters and reducing unnecessary interference factors. By combining the target monitoring strategy with the indicators, the accuracy of monitoring is improved, providing accurate data support for mine safety management and effectively reducing the risk of safety accidents caused by gas problems.

[0034] In an embodiment of this application, obtaining the gas data collected by sensors set in different areas of the goaf and generating a gas concentration matrix with time and space correlation based on the gas data includes: Segmenting the gas data according to the first time window to generate a time series matrix; Constructing an adjacency matrix according to the spatial topological relationship between the deployment positions of all sensors; Fusing the time series matrix and the adjacency matrix through a spatio-temporal graph convolutional network to obtain the gas concentration matrix.

[0035] In this embodiment, the length of the first time window needs to be determined according to the characteristics of gas changes in the goaf and the actual requirements of monitoring; among them, the change of gas concentration in the goaf is affected by various factors, such as the operation cycle of the ventilation system, the frequency of coal mining operations, etc. Among them, if the ventilation system adjusts the air volume once an hour, the first time window can be set to 1 hour so that the segmentation result of the data can capture the law of gas concentration change with ventilation.

[0036] In this embodiment, the continuous gas data collected by the sensor is segmented at intervals of the first time window. For example, assuming that the sensor continuously collects gas data for 24 hours and the first time window is 1 hour, then the 24-hour gas data will be divided into 24 time periods. For the gas data within each time period, the data collected by different sensors are arranged into a vector in the order of the sensor numbers. For example, if 5 sensors are set in the gob area and the gas concentrations collected by the sensors within a certain 1-hour time period are c1, c2, c3, c4, c5 respectively, then a vector [c1, c2, c3, c4, c5] can be formed. The vectors corresponding to each time period are arranged in chronological order to obtain a time series matrix. Assuming that a total of M time periods are divided and there are N sensors, the dimension of the time series matrix is M×N.

[0037] In this embodiment, according to the deployment positions of all sensors in the gob area, the spatial topological relationship between the sensors is determined. For example, if two sensors are close to each other and on the same ventilation path, then the correlation between them is strong; on the contrary, if two sensors are separated by obstacles or in different ventilation zones, then the correlation between them is weak. In this embodiment, an N×N adjacency matrix is constructed according to the spatial topological relationship, where N is the number of sensors. For the element Aij in the matrix, if there is a spatial correlation between sensor i and sensor j, then Aij is assigned a value of 1; if there is no correlation, then Aij is assigned a value of 0. It is also possible to use the value obtained by normalizing the reciprocal of the physical distance between the sensors as the value of the matrix element.

[0038] This embodiment uses a spatio-temporal graph convolutional network to process the time series features of gas data and extract the features of gas data in the time dimension. At the same time, the spatial features between the sensors in the adjacency matrix are extracted. The time features and spatial features are fused through the fusion layer in the spatio-temporal graph convolutional network to generate a gas concentration matrix. Among them, the gas concentration matrix includes the gas concentration values of each sensor at different time points, contains the spatial relationship information between the sensors, and can more comprehensively and accurately describe the distribution and change of gas concentration in the gob area.

[0039] In an embodiment of the present application, segmenting the gas data according to the first time window to generate a time series matrix includes: Calculating the initial time window length according to the operating parameters of the ventilator set in the gob area, where the operating parameters include the start-stop cycle of the ventilator, the average wind speed in the gob area, and the gas diffusion time constant; Extracting the periodic characteristics of the gas concentration fluctuations in the historical gas data, and taking the period corresponding to the dominant frequency in the periodic characteristics identified by the Fourier transform algorithm as the window adjustment factor; Perform a weighted calculation on the initial time window length and the window adjustment factor to obtain the first time window; Segment the gas data according to the first time window to generate a time series matrix.

[0040] In this embodiment, the start and stop of the ventilator directly affect the air flow state in the gob area, thereby changing the gas diffusion and distribution laws; obtain the historical start and stop records of the ventilator and analyze its start and stop cycle. For example, if the ventilator starts once every 6 hours and runs continuously for 2 hours, this 6-hour cycle interval is the start and stop cycle. In this embodiment, the average wind speed in the gob area is obtained by performing a weighted average on the wind speed data at multiple positions within a period of time (such as the past 24 hours). For example, the wind speeds in different areas such as near the air inlet, air outlet, and the middle of the roadway are calculated according to the weights of their influence on gas diffusion to obtain the comprehensive average wind speed. The gas diffusion time constant in this embodiment describes the diffusion characteristics of gas in the gob area medium, and the gas diffusion time constant is related to the geological structure of the gob area (such as rock porosity, fracture development degree) and the properties of the gas itself (such as molecular diffusion coefficient).

[0041] In this embodiment, the initial time window length is calculated according to the ventilator start and stop cycle T, the average wind speed V in the gob area, the gas diffusion time constant τ, and the first formula. Among them, the first formula is:

[0042] Among them, is the initial time window length, L is the ventilation path length in the gob area, and α, β, γ are weight coefficients adjusted according to the actual situation.

[0043] In this embodiment, first, perform preprocessing operations such as denoising and missing value filling on the historical gas data; among them, median filtering is used to remove random noise in the data. For missing data, the data of adjacent time points and adjacent sensors are used to fill them through linear interpolation or machine learning-based methods. Secondly, take the preprocessed historical gas concentration data as a time domain signal and apply the Fourier transform algorithm to convert it to the frequency domain; in the frequency domain, each frequency corresponds to a specific period, and the higher the frequency, the shorter the corresponding period; by analyzing the spectrogram of the frequency domain signal, find the frequency component with the largest energy proportion, and this frequency is the dominant frequency, and take the period corresponding to the dominant frequency as the window adjustment factor. For example, if the dominant frequency is f = 0.1 Hz, then the corresponding period indicates that the gas concentration will have a relatively significant periodic fluctuation every 10 hours.

[0044] In an embodiment of the present application, Establishing a gas diffusion model based on the geological structure data and ventilation parameters of the gob area includes: Perform three-dimensional grid discretization on the goaf geological structure data, and extract the geological attribute parameters of each grid cell, including the permeability tensor and porosity; Solve the Darcy equation based on the ventilation parameters and geological attribute parameters to obtain the first result. The ventilation parameters include gas pressure; Establish a gas diffusion model based on the first result and the diffusion effect.

[0045] In this embodiment, technologies such as geographic information system and three-dimensional laser scanning are used to obtain the goaf geological structure data, including the spatial form, rock layer distribution, fracture development situation, etc. of the goaf, and a three-dimensional geological model is constructed according to the geological structure data. For example, the goaf is scanned by three-dimensional laser scanning technology to obtain the point cloud data of the goaf, and then the point cloud data is converted into a three-dimensional solid model using professional software.

[0046] In this embodiment, the finite element or finite volume method can be used to perform grid discretization on the three-dimensional geological model, and the goaf is divided into a large number of small three-dimensional grid cells. The size and density of the grid are adjusted according to the complexity of the goaf geological structure and the calculation accuracy requirements. In areas with complex geological structures and developed fractures, the grid is refined to improve the calculation accuracy; in areas with relatively simple geological structures, the grid size is appropriately increased to reduce the calculation amount. For each grid cell, its geological attribute parameters are extracted: the permeability tensor and porosity.

[0047] In this embodiment, based on the ventilation parameters of the goaf (such as gas pressure, which can be obtained by real-time measurement at different positions in the goaf through a pressure sensor) and the extracted geological attribute parameters (permeability tensor and porosity), a Darcy equation model applicable to gas flow in the goaf is established.

[0048]

[0049] Among them, is the Darcy velocity, that is, the gas seepage velocity, k is the permeability tensor, μ is the gas dynamic viscosity, is the pressure gradient.

[0050] In this embodiment, numerical calculation methods (such as the finite element method, finite difference method, etc.) are used to solve the Darcy equation; the boundary conditions of the goaf (such as the gas pressure and flow rate at the air inlet, the pressure at the air outlet, etc.) and the initial conditions (such as the gas pressure and concentration of each grid cell at the initial moment) are substituted into the equation, and through iterative calculation, the gas seepage velocity of each grid cell at different moments is solved, which is the first result.

[0051]

[0052] Among them, where 𝜙 is the porosity, t is the time, c is the gas concentration, D is the effective diffusion coefficient tensor (including molecular diffusion and mechanical dispersion), and S is the source term (such as gas emission); represents the net outflow of the gas concentration c due to the velocity field If the divergence is positive, the gas in this area diffuses outwards; if it is negative, the gas accumulates here.

[0053] In one embodiment of the present application, determine the target monitoring strategy based on the deviation coefficient matrix of the gas concentration matrix and the gas concentration prediction matrix, including: The gas concentration matrix includes concentration data of multiple different gases, the gas concentration prediction matrix includes concentration prediction data of multiple different gases, the deviation coefficient matrix includes multiple deviation coefficients, and each deviation coefficient is the difference between the concentration data of a gas and the concentration prediction data of the corresponding gas; For each gas: In response to the deviation coefficient between the concentration data of the gas and the concentration prediction data of the corresponding gas being less than or equal to the first threshold, determine the target monitoring strategy from multiple standard monitoring strategies; In response to the deviation coefficient between the concentration data of the gas and the concentration prediction data of the corresponding gas being greater than the first threshold and less than or equal to the second threshold, determine the first monitoring strategy from multiple standard monitoring strategies, and adjust the monitoring frequency of the sensor in the first monitoring strategy based on a preset first value to obtain the target monitoring strategy; In response to the deviation coefficient between the concentration data of the gas and the concentration prediction data of the corresponding gas being greater than the second threshold, select the emergency monitoring strategy, and the emergency monitoring strategy is used as the target monitoring strategy; The second threshold is greater than the first threshold.

[0054] In this embodiment, the gas concentration matrix covers the actual concentration data of multiple different gases (such as carbon monoxide, gas, oxygen, etc.) at different times and spatial positions; the gas concentration prediction matrix is the predicted concentration data of these gases at the same time and spatial positions calculated by the gas diffusion model; the deviation coefficient matrix is composed of multiple deviation coefficients, and each deviation coefficient is the difference between the actual concentration data of a certain gas and the corresponding predicted concentration data.

[0055] In this embodiment, when the deviation coefficient between the concentration data of a certain gas and the corresponding predicted concentration data is less than or equal to the first threshold, it indicates that the actual gas concentration is relatively close to the predicted concentration, and the gas concentration change is within the predictable normal range. At this time, a suitable strategy is selected from multiple pre-formulated standard monitoring strategies as the target monitoring strategy. The standard monitoring strategies are usually formulated according to the conventional monitoring requirements of the goaf, such as collecting data from all sensors at fixed time intervals (e.g., once per hour) and conducting regular manual inspections. Among them, the first threshold is a value preset according to the accuracy requirements of gas monitoring and the actual situation of the goaf.

[0056] In this embodiment, when the deviation coefficient is between the first threshold and the second threshold, it indicates that the gas concentration has changed abnormally to a certain extent but is still within the controllable range. At this time, a relatively stricter first monitoring strategy is selected from multiple standard monitoring strategies. The monitoring frequency of the sensors in the first monitoring strategy is adjusted based on a preset first value. Suppose the first value is 2. If the monitoring frequency of the sensors in the first monitoring strategy is once every half hour, then the adjusted monitoring frequency of the sensors becomes once every 15 minutes, thereby obtaining the target monitoring strategy to capture the gas concentration change more timely. Among them, the second threshold is greater than the first threshold.

[0057] In this embodiment, when the deviation coefficient is greater than the second threshold, it indicates that there is a significant deviation between the actual value and the predicted value of the gas concentration, and there may be potential dangerous situations such as abnormal ventilation and gas leakage, and emergency measures need to be taken immediately. At this time, the pre-formulated emergency monitoring strategy is selected as the target monitoring strategy. The emergency monitoring strategy usually includes significantly increasing the monitoring frequency of the sensors (such as collecting data once per minute), increasing the frequency and scope of manual inspections, and activating the alarm system to notify relevant personnel, etc., to ensure that dangerous situations can be quickly responded to and handled.

[0058] This embodiment's hierarchical decision-making mechanism based on the deviation coefficient matrix can adjust the monitoring strategy according to the gas concentration change situation, improving the effectiveness and safety of goaf gas monitoring.

[0059] In an embodiment of the present application, monitoring the goaf based on the target monitoring strategy includes: Determining the target monitoring index from multiple standard monitoring indexes based on the mining information and gas hazard level of the goaf; Monitoring the goaf based on the target monitoring index.

[0060] In this embodiment, the current mining progress of the goaf is obtained to clarify the areas being mined, the areas that have been mined, and the areas to be mined. For example, through the mine mining plan and real-time mining records, it is determined that at a certain moment, the mining operation is in progress in area A of the goaf, area B has been mined and is in a closed state, and area C is the mining target area for the next stage. There are differences in the gas generation and distribution characteristics in different mining stages and areas. In the areas being mined, due to the fragmentation of rock strata and mechanical disturbances, more gases such as gas are released; in the closed areas, there will be gas accumulation phenomena.

[0061] In this embodiment, according to the gas concentration matrix and the deviation coefficient matrix, combined with the dangerous characteristics of different gases (such as the flammability and explosiveness of gas, the toxicity of carbon monoxide, etc.), the danger level of each gas is evaluated; the overall gas danger level of the goaf is determined by synthesizing the danger levels of each gas.

[0062] This embodiment uses the gas diffusion model and actual monitoring data to judge the diffusion range and speed of dangerous gases. If a certain dangerous gas rapidly diffuses to a large area in a short time, it indicates a relatively high danger level. For example, when gas rapidly diffuses to multiple roadways under poor ventilation conditions, more stringent monitoring measures need to be taken.

[0063] In this embodiment, the standard monitoring index library is an index library established in advance containing various standard monitoring indexes, such as sensor numbers and gas concentration ranges, etc.; these standard monitoring indexes are formulated according to different goaf conditions and safety requirements.

[0064] In an embodiment of the present application, the standard monitoring indexes include: standard monitoring equipment numbers and standard data ranges; the target monitoring indexes include: target monitoring equipment numbers and target data ranges. Based on the mining information of the goaf and the gas danger level, the target monitoring indexes are determined from multiple standard monitoring indexes, including: Determining the target monitoring equipment number from multiple standard monitoring equipment based on the mining information of the goaf; Determining the target data range from multiple standard data ranges based on the mining information of the goaf and the gas danger level; Taking the target monitoring equipment number and the target data range as the target monitoring indexes.

[0065] In this embodiment, according to the goaf mining design drawings and the real-time mining progress, the goaf is divided into different functional areas, such as mining operation areas, pre-mining preparation areas, closed goafs, etc. For example, according to the mining plan, the current operation area is located in the eastern area of the goaf, where the rock strata are fragmented due to continuous mining activities, and the gas release risk is relatively high; while there will be gas accumulation problems in the western closed area.

[0066] Analyze the characteristics of mining activities in each area, including mining techniques (such as fully mechanized mining, ordinary mining), equipment operation conditions (start-up and shutdown times, working intensity of equipment such as shearers, conveyors, etc.), and personnel operation distribution. For example, in the fully mechanized mining operation area, large-scale mechanical equipment operates frequently, which will cause great disturbance to the surrounding gas flow, and equipment friction will generate heat, increasing the risk level of combustible gas.

[0067] In this embodiment, according to the established correspondence library between standard monitoring equipment numbers and goaf areas, select specific monitoring positions and functions corresponding to each standard monitoring equipment number. For example, the equipment numbered S-001 is a gas concentration sensor, deployed near the air inlet, for monitoring the gas content in the fresh air; the equipment numbered S-010 is a carbon monoxide sensor, located in the return airway of the working face, mainly for monitoring the carbon monoxide concentration generated by the operation. This example can screen out suitable equipment from the standard monitoring equipment according to the characteristics and risk levels of the mining areas. For high-risk mining operation areas, preferentially select equipment with high precision and fast response speed, such as gas sensors with real-time continuous monitoring functions, and increase the number of equipment deployments; while in the already closed goaf, reduce the number of equipment and retain the monitoring equipment for key gases (such as oxygen, carbon dioxide) to ensure that abnormal gas accumulation can be detected in time. The determined equipment number is the target monitoring equipment number.

[0068] In this embodiment, combine information such as mining activity intensity, progress, and ventilation conditions to evaluate the trend of gas generation and diffusion. For example, when the mining speed increases, the gas emission volume of gas such as methane will increase significantly; if the ventilation system fails or is adjusted, it will cause gas accumulation, thereby increasing the risk level.

[0069] In this embodiment, according to the characteristics and danger thresholds of different gases, divide the gas danger levels. Taking methane as an example, its danger levels are divided into three levels: low (concentration < 0.5%), medium (0.5% ≤ concentration < 1%), and high (concentration ≥ 1%); carbon monoxide is divided into safe (concentration < 24 ppm), warning (24 ppm ≤ concentration < 50 ppm), and dangerous (concentration ≥ 50 ppm) levels.

[0070] In this embodiment, the standard data range is the normal data range and alarm threshold range preset for different monitoring equipment and gas types. This embodiment will also dynamically adjust the standard data range according to the mining information and gas danger levels. In high-risk areas, reduce the data range and lower the warning and alarm thresholds to detect gas anomalies more timely; while in low-risk areas, expand the data range to reduce false alarms. Among them, the adjusted data range is the target data range.

[0071] Corresponding to the gas monitoring method for the goaf of a mine in the above embodiment, Figure 2The structural block diagram of the gas monitoring device for the mined - out area of a mine provided by an embodiment of the present application. For the sake of convenience, only the parts related to the embodiments of the present application are shown. Refer to Figure 2 The gas monitoring device 20 for the mined - out area of the mine includes: a matrix generation module 21, a prediction matrix module 22, a strategy determination module 23, and a gas monitoring module 24.

[0072] Among them, the matrix generation module 21 is used to obtain the gas data collected by sensors set in different areas of the mined - out area, and generate a gas concentration matrix with time and space correlations based on the gas data; The prediction matrix module 22 is used to establish a gas diffusion model based on the geological structure data and ventilation parameters of the mined - out area, and input the gas data into the gas diffusion model to calculate the gas concentration prediction matrix of the mined - out area; The strategy determination module 23 is used to determine the target monitoring strategy based on the deviation coefficient matrix of the gas concentration matrix and the gas concentration prediction matrix; The gas monitoring module 24 is used to monitor the mined - out area based on the target monitoring strategy.

[0073] In an embodiment of the present application, the matrix generation module 21 is specifically used for: Segment the gas data according to the first time window to generate a time - series matrix; Construct an adjacency matrix according to the spatial topological relationship between the deployment positions of all sensors; Fuse the time - series matrix and the adjacency matrix through a spatio - temporal graph convolutional network to obtain the gas concentration matrix.

[0074] In an embodiment of the present application, the matrix generation module 21 is specifically used for: Calculate the initial time - window length according to the operating parameters of the ventilator set in the mined - out area, where the operating parameters include the start - stop cycle of the ventilator, the average wind speed in the mined - out area, and the gas diffusion time constant; Extract the periodic characteristics of the gas concentration fluctuations in the historical gas data, and use the period corresponding to the dominant frequency in the periodic characteristics identified by the Fourier transform algorithm as the window adjustment factor; Perform weighted calculation on the initial time - window length and the window adjustment factor to obtain the first time window; Segment the gas data according to the first time window to generate a time - series matrix.

[0075] In an embodiment of the present application, the prediction matrix module 22 is specifically used for: Perform three - dimensional grid discretization processing on the geological structure data of the mined - out area, and extract the geological attribute parameters of each grid unit, including the permeability tensor and porosity; Solve the Darcy equation according to the ventilation parameters and geological attribute parameters to obtain a first result, where the ventilation parameters include gas pressure; Establish a gas diffusion model based on the first result and the diffusion effect.

[0076] In an embodiment of the present application, the policy determination module 23 is specifically configured to: The gas concentration matrix includes concentration data of multiple different gases, the gas concentration prediction matrix includes concentration prediction data of multiple different gases, and the deviation coefficient matrix includes multiple deviation coefficients, and each deviation coefficient is the difference between the concentration data of the gas and the concentration prediction data of the corresponding gas; For each gas: In response to the deviation coefficient between the concentration data of the gas and the corresponding concentration prediction data of the gas being less than or equal to a first threshold, determine a target monitoring policy from multiple standard monitoring policies; In response to the deviation coefficient between the concentration data of the gas and the corresponding concentration prediction data of the gas being greater than the first threshold and less than or equal to a second threshold, determine a first monitoring policy from multiple standard monitoring policies, and adjust the monitoring frequency of the sensor in the first monitoring policy based on a preset first value to obtain a target monitoring policy; In response to the deviation coefficient between the concentration data of the gas and the corresponding concentration prediction data of the gas being greater than the second threshold, select an emergency monitoring policy, and the emergency monitoring policy is used as the target monitoring policy; The second threshold is greater than the first threshold.

[0077] In an embodiment of the present application, the gas monitoring module 24 is specifically configured to: Determine a target monitoring index from multiple standard monitoring indexes based on the mining information and gas hazard level of the goaf; Monitor the goaf based on the target monitoring index.

[0078] In an embodiment of the present application, the gas monitoring module 24 is specifically configured to: The standard monitoring indexes include: standard monitoring equipment numbers and standard data intervals; the target monitoring indexes include: target monitoring equipment numbers and target data intervals; Determining a target monitoring index from multiple standard monitoring indexes based on the mining information and gas hazard level of the goaf includes: Determine the target monitoring equipment number from multiple standard monitoring equipment based on the mining information of the goaf; Determine the target data interval from multiple standard data intervals based on the mining information and gas hazard level of the goaf; Use the target monitoring equipment number and the target data interval as the target monitoring index.

[0079] See Figure 3 ,Figure 3 A schematic block diagram of an electronic device provided in an embodiment of the present application. As Figure 3 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through a communication bus 305. The memory 304 is used to store computer programs, and the computer programs include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module in the above-mentioned device embodiments, such as Figure 2 shown, the functions of the matrix generation module 21, the prediction matrix module 22, the policy determination module 23, and the gas monitoring module 24.

[0080] It should be understood that in the embodiment of the present application, the so-called processor 301 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0081] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0082] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0083] In specific implementation, the processors 301, input devices 302, and output devices 303 described in the embodiments of the present application may implement the implementation manners described in the first and second embodiments of the gas monitoring method for the mined-out area of the mine provided in the embodiments of the present application, and may also implement the implementation manner of the electronic device described in the embodiments of the present application, which will not be elaborated here.

[0084] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the methods of the above embodiments are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0085] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk equipped on the electronic device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store the data that has been output or will be output.

[0086] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0087] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0088] In several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection to each other can be an indirect coupling or communication connection through some interfaces or units, or can also be an electrical, mechanical or other form of connection.

[0089] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can also be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

[0090] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0091] The above are only the specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for monitoring gas in a mined - out area of a mine, characterized in that, Including: Obtaining gas data collected by sensors set in different regions of the goaf, and generating a gas concentration matrix with time and space associations based on the gas data; Establishing a gas diffusion model based on the geological structure data and ventilation parameters of the goaf, and inputting the gas data into the gas diffusion model to calculate a gas concentration prediction matrix of the goaf; Determining a target monitoring strategy based on a deviation coefficient matrix of the gas concentration matrix and the gas concentration prediction matrix; Monitoring the goaf based on the target monitoring strategy.

2. The gas monitoring method for mined - out areas in mines according to claim 1, characterized in that, The obtaining gas data collected by sensors set in different regions of the goaf, and generating a gas concentration matrix with time and space associations based on the gas data, includes: Segmenting the gas data according to a first time window to generate a time series matrix; Constructing an adjacency matrix according to the spatial topological relationship between the deployment positions of all sensors; Fusing the time series matrix and the adjacency matrix through a spatio-temporal graph convolutional network to obtain a gas concentration matrix.

3. The gas monitoring method for a mined - out area of a mine according to claim 2, wherein, The segmenting the gas data according to a first time window to generate a time series matrix, includes: Calculating an initial time window length according to the operating parameters of a ventilator set in the goaf, where the operating parameters include the start-stop cycle of the ventilator, the average wind speed in the goaf, and the gas diffusion time constant; Extracting the periodic characteristics of the gas concentration fluctuations in the historical gas data, and taking the period corresponding to the dominant frequency in the periodic characteristics identified by the Fourier transform algorithm as a window adjustment factor; Performing weighted calculation on the initial time window length and the window adjustment factor to obtain a first time window; Segmenting the gas data according to the first time window to generate a time series matrix.

4. The gas monitoring method for mined - out areas in mines according to claim 1, wherein, The establishing a gas diffusion model based on the geological structure data and ventilation parameters of the goaf includes: Performing three-dimensional grid discretization processing on the goaf geological structure data, and extracting the geological attribute parameters of each grid unit, including the permeability tensor and porosity; Solving the Darcy equation according to the ventilation parameters and geological attribute parameters to obtain a first result, where the ventilation parameters include gas pressure; Establishing a gas diffusion model based on the first result and the diffusion effect.

5. The gas monitoring method for the mined - out area of a mine according to claim 1, characterized in that, The determining a target monitoring strategy based on a deviation coefficient matrix of the gas concentration matrix and the gas concentration prediction matrix, includes: The gas concentration matrix includes concentration data of multiple different gases, the gas concentration prediction matrix includes concentration prediction data of multiple different gases, the deviation coefficient matrix includes multiple deviation coefficients, and each deviation coefficient is the difference between the concentration data of a gas and the concentration prediction data of the corresponding gas; For each gas: In response to the deviation coefficient of the concentration data of the gas and the concentration prediction data of the corresponding gas being less than or equal to a first threshold, determining a target monitoring strategy from multiple standard monitoring strategies; In response to the deviation coefficient of the concentration data of the gas and the concentration prediction data of the corresponding gas being greater than the first threshold and less than or equal to a second threshold, determining a first monitoring strategy from multiple standard monitoring strategies, and adjusting the monitoring frequency of the sensors in the first monitoring strategy based on a preset first value to obtain a target monitoring strategy; In response to the deviation coefficient between the concentration data of the gas and the predicted concentration data of the corresponding gas being greater than a second threshold, an emergency monitoring strategy is selected, and the emergency monitoring strategy is used as the target monitoring strategy; The second threshold is greater than the first threshold.

6. The gas monitoring method for mined - out areas in mines according to claim 1, wherein, The monitoring of the goaf based on the target monitoring strategy includes: Determining target monitoring indicators from multiple standard monitoring indicators based on the mining information of the goaf and the gas hazard level; Monitoring the goaf based on the target monitoring indicators.

7. The gas monitoring method for mined - out areas in mines according to claim 6, characterized in that, The standard monitoring indicators include: standard monitoring equipment numbers and standard data ranges; the target monitoring indicators include: target monitoring equipment numbers and target data ranges; The determining of the target monitoring indicators from multiple standard monitoring indicators based on the mining information of the goaf and the gas hazard level includes: Determining the target monitoring equipment number from multiple standard monitoring equipment based on the mining information of the goaf; Determining the target data range from multiple standard data ranges based on the mining information of the goaf and the gas hazard level; Using the target monitoring equipment number and the target data range as the target monitoring indicators.

8. A gas monitoring device for a mined-out area in a mine, characterized in that, Including: A matrix generation module, configured to obtain gas data collected by sensors arranged in different regions of the goaf, and generate a gas concentration matrix with time and space correlations based on the gas data; A prediction matrix module, configured to establish a gas diffusion model based on the geological structure data and ventilation parameters of the goaf, and input the gas data into the gas diffusion model to calculate the gas concentration prediction matrix of the goaf; A strategy determination module, configured to determine the target monitoring strategy based on the deviation coefficient matrix of the gas concentration matrix and the gas concentration prediction matrix; A gas monitoring module, configured to monitor the goaf based on the target monitoring strategy.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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